You are asked to demonstrate your recently acquired skills in risk management in a business context. The project will also prepare you gradually for the final Risk Management hedging project.
EverRight Securities.EverRight hires MBAs both for their expertise and professionalism in communication with clients.EverRight was recently approached by GreatPlains to produce a fact-based short paper to understand context and risk associated with Oil and Natural Gas in Canada.GreatPlains is a consortium of senior executives with a background in extracting industries and require this knowledge to better understand the economics of monetizing resources after a molecule is produced.Use of APIs for Enterprise workflow i.e. strictly no Excel, csv or use of other local files.
Data wrangling and parsing. You must use tidy data with long and wide data frames.
Choice of effective visualization in communication.
Data analytics and an ability to tell a story from it.
Foundational risk management skills.
Clean, clear and concise rendering of a business document. You may refer to the Rmarkdown cheatsheet or any other online document to customize your output. I reserve the right to award a maximum of 50% for any document not properly presented. Some characteristics of a properly presented document:
Each question should be answered as if you were presenting to a client: you would not show code to a client!
Learning to learn: Using new packages and functions as you see fit to achieve the desired outcome.
Whilst there are many packages to format tables the following are suggested:
You will use the following sources of data:
You will be using data from the US Energy Information Administration and the API webpage is available at EIA Open Data. You may refer to the bootcamp section 7.3 after you have registered and obtained your own API key. The eia, EIAdata and RTL packages provide functions to access the API once you have your API key.
Morningstar Commodities for futures contracts using RTL::getPrices() and your credentials.
The RTL::fizdiffs randomized data sets of Crude oil prices in key trading locations across North America:
Simplified NA Energy Infrastructure Maps
Using Rmarkdown, render into a clean, clear, concise html document to be sent to the client.
Name your file firstNameLastName.Rmd and send it to pcote@ualberta.ca.
GreatPlains needs context on the level and trends of Canadian exports to the US since 2010-01-01.
Use the following data from the EIA to tell the story.
The benchmark crude oil grade in Alberta is Western Canadian Select ("WCS"). GreatPlains executives need factual information on the value of WCS a producer may obtain by selling in Alberta or exporting to Houston. This export additional value is then compared to the costs and risks of taking on pipeline commitments.
Using the RTL::fizdiffs randomized dataset below, prepare the following in your report:
RTL::fizdiffs %>%
dplyr::transmute(date = date,
Alberta = WTI.CMA01 + WCS.HDY,
Houston = WTI.CMA01 + WCS.HOU)
## # A tibble: 1,300 × 3
## date Alberta Houston
## <date> <dbl> <dbl>
## 1 2022-02-11 77.3 85.5
## 2 2022-02-10 74.4 82.8
## 3 2022-02-09 74.7 82.4
## 4 2022-02-08 73.5 82.1
## 5 2022-02-07 75.3 83.8
## 6 2022-02-04 76.1 84.9
## 7 2022-02-03 74.4 82.8
## 8 2022-01-31 73.1 81.7
## 9 2022-01-28 72.2 80.6
## 10 2022-01-27 72.2 80.4
## # … with 1,290 more rows
GreatPlains clients have at times to make decisions on temporary storage and all they see in the financial news headlines is the price of WTI crude.
They are considering making decisions on storing from the 2nd to the 3rd contract month.
You, as a specialist, know that there are futures contracts for different delivery months and using data from Morningstar from 2010-01-01 to 2022-01-31:
mapply() as covered in class or purrr::pmap() if you want to experiment. We will use the latter for more complex risk management problems at a later stage.GreatPlains often has to provide advice on matters of financing terms. In this specific case, the client has the choice of financing for:
The problem they have is that their usual decision-making process is of low quality: they have no idea as of today what the implied refinancing rate is and therefore engage in broad qualitative conversations around the future path of interest rates.
bankOffer
## # A tibble: 5 × 2
## maturity rate
## <dbl> <dbl>
## 1 1 0.03
## 2 3 0.035
## 3 5 0.04
## 4 7 0.0425
## 5 10 0.05
This is strictly an individual exercise. Any attempt to communicate or cooperate with any other person will be deemed a violation of Academic Integrity and Honesty.
See Grading section of the class notes for more details.